{
  "abstract": "Long-term EEG monitoring is central to rodent epilepsy research, and manual review is laborious because recordings span hundreds to tens of thousands of animal-hours. Published automated detectors perform well on their development datasets but are typically fixed models that may not adapt to another laboratory's electrodes, animals, or noise conditions. We introduce NED-Net, open-source software for training and maintaining seizure detectors. It uses reviewed candidate events as training labels, runs detection live during recording, and retrains as annotations accumulate. A detector trained on one mouse cohort (867 annotated seizures) achieved 93.6% precision in human review of 171 detections. Applied unchanged to a second cohort on the same acquisition system, its detections were mostly seizures (12 of 13 reviewed), but per-animal recall varied substantially (median 10%), with no convulsive reference seizure detected in four of 11 animals. Retraining with annotations from the second cohort raised convulsive recall from 15% to 64%, with at least one reference seizure detected in every animal. On recordings withheld from training, 75% of detections were seizures and 14 of 22 reference seizures were detected, whereas the source model and a model trained without these animals detected none. NED-Net supports evaluating and adapting detectors to local recordings.",
  "abstract_shown": true,
  "affiliations": [
    "Epilepsy Center, Lund University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "M. Y. MIR"
    },
    {
      "affs": [
        0
      ],
      "name": "M. Ledri"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dw9db57qrt559.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.15.759362",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.15.759362v1.full",
  "id": "dw9db57qrt559",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.15.759362v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ],
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W7223152499",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.15.759362v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dw9db57qrt559.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:55Z",
      "native_id": "10.64898/2026.10.15.759362",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.15.759362v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "NED-Net: an annotation-training loop for rodent EEG event detection",
  "title_text": "NED-Net: an annotation-training loop for rodent EEG event detection",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:55Z",
  "url": "https://research.draconitic.com/paper/dw9db57qrt559",
  "venue": "bioRxiv",
  "versions": []
}